docstr: Validate the output dimensions of a WaveRNN model.
docstr_tok: ["Validate","the","output","dimensions","of","a","WaveRNN","model","."]
code: def test_waveform(self):
      """Validate the output dimensions of a WaveRNN model."""
  
      upsample_scales = [5, 5, 8]
      n_rnn = 512
      n_fc = 512
      n_classes = 512
      hop_length = 200
      n_batch = 2
      n_time = 200
      n_freq = 100
      n_output = 256
      n_res_block = 10
      n_hidden = 128
      kernel_size = 5
  
      model = WaveRNN(
          upsample_scales,
          n_classes,
          hop_length,
          n_res_block,
          n_rnn,
          n_fc,
          kernel_size,
          n_freq,
          n_hidden,
          n_output,
      )
  
      x = torch.rand(
          n_batch, 1, hop_length * (n_time - kernel_size + 1)
      )
      mels = torch.rand(n_batch, 1, n_freq, n_time)
      out = model(x, mels)
  
      assert out.size() == (
          n_batch,
          1,
          hop_length * (n_time - kernel_size + 1),
          n_classes,
      )
  
imports: import itertools
  from collections import namedtuple
  import torch
  from parameterized import parameterized
  from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
  from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import torch_script
label_desc: Use image node_id 1 for calling the TestWaveRNN obj's underlying member method code with example usage: obj.test_waveform() without return types